'''simple docstring'''




import colorsys

from PIL import Image  # type: ignore






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       float:
    __lowerCamelCase            =    x
    __lowerCamelCase            =    y
    for step in range(UpperCamelCase__							):  # noqa: B007
        __lowerCamelCase            =    a * a - b * b + x
        __lowerCamelCase            =    2 * a * b + y
        __lowerCamelCase            =    a_new

        # divergence happens for all complex number with an absolute value
        # greater than 4
        if a * a + b * b > 4:
            break
    return step / (max_step - 1)






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       tuple:
    if distance == 1:
        return (0, 0, 0)
    else:
        return (2_55, 2_55, 2_55)






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       tuple:
    if distance == 1:
        return (0, 0, 0)
    else:
        return tuple(round(i * 2_55							) for i in colorsys.hsv_to_rgb(UpperCamelCase__							,    1							,    1							)							)






def __lowerCAmelCase					(				UpperCamelCase__ = 8_00							,    UpperCamelCase__ = 6_00							,    UpperCamelCase__ = -0.6							,    UpperCamelCase__ = 0							,    UpperCamelCase__ = 3.2							,    UpperCamelCase__ = 50							,    UpperCamelCase__ = True							,    )      ->       Image.Image:
    __lowerCamelCase            =    Image.new('''RGB'''							,    (image_width, image_height)							)
    __lowerCamelCase            =    img.load()

    # loop through the image-coordinates
    for image_x in range(UpperCamelCase__							):
        for image_y in range(UpperCamelCase__							):
            # determine the figure-coordinates based on the image-coordinates
            __lowerCamelCase            =    figure_width / image_width * image_height
            __lowerCamelCase            =    figure_center_x + (image_x / image_width - 0.5) * figure_width
            __lowerCamelCase            =    figure_center_y + (image_y / image_height - 0.5) * figure_height

            __lowerCamelCase            =    get_distance(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)

            # color the corresponding pixel based on the selected coloring-function
            if use_distance_color_coding:
                __lowerCamelCase            =    get_color_coded_rgb(UpperCamelCase__							)
            else:
                __lowerCamelCase            =    get_black_and_white_rgb(UpperCamelCase__							)

    return img


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  # colored version, full figure
  __UpperCAmelCase					=get_image()

  # uncomment for colored version, different section, zoomed in
  # img = get_image(figure_center_x = -0.6, figure_center_y = -0.4,
  # figure_width = 0.8)

  # uncomment for black and white version, full figure
  # img = get_image(use_distance_color_coding = False)

  # uncomment to save the image
  # img.save("mandelbrot.png")

  img.show()

'''simple docstring'''




# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__UpperCAmelCase					={
    "configuration_efficientnet": [
        "EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientNetConfig",
        "EfficientNetOnnxConfig",
    ]
}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=["EfficientNetImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __UpperCAmelCase					=[
      "EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EfficientNetForImageClassification",
      "EfficientNetModel",
      "EfficientNetPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_efficientnet import (
      EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
      EfficientNetConfig,
      EfficientNetOnnxConfig,
  )

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .image_processing_efficientnet import EfficientNetImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_efficientnet import (
        EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
        EfficientNetForImageClassification,
        EfficientNetModel,
        EfficientNetPreTrainedModel,
    )

else:
  import sys

  __UpperCAmelCase					=_LazyModule(__name__, globals()["__file__"], _import_structure)
